code: def std_of_l2_normalized(z: torch.Tensor) -> torch.Tensor:
      """Calculates the mean of the standard deviation of z along each dimension.
  
      This measure was used by [0] to determine the level of collapse of the
      learned representations. If the returned number is 0., the outputs z have
      collapsed to a constant vector. "If the output z has a zero-mean isotropic
      Gaussian distribution" [0], the returned number should be close to 1/sqrt(d)
      where d is the dimensionality of the output.
  
      [0]: https://arxiv.org/abs/2011.10566
  
      Args:
          z:
              A torch tensor of shape batch_size x dimension.
  
      Returns:
          The mean of the standard deviation of the l2 normalized tensor z along
          each dimension.
  
      """
  
      if len(z.shape) != 2:
          raise ValueError(
              f"Input tensor must have two dimensions but has {len(z.shape)}!"
          )
  
      z_norm = torch.nn.functional.normalize(z, dim=1)
      return torch.std(z_norm, dim=0).mean()
  
file_path: lightly/lightly/utils/debug.py
imports: from typing import List, Union
  import torch
  import torchvision
  from PIL import Image
  from lightly.data.collate import BaseCollateFunction, MultiViewCollateFunction
label_desc: Use image node_id 2 for calling a global function with example usage: std_of_l2_normalized(z) and returns: torch